Abstract
As the application of a coal mine Internet of Things (IoT), mobile measurement devices, such as intelligent mine lamps, cause moving measurement data to be increased. How to transmit these large amounts of mobile measurement data effectively has become an urgent problem. This paper presents a compressed sensing algorithm for the large amount of coal mine IoT moving measurement data based on a multi-hop network and total variation. By taking gas data in mobile measurement data as an example, two network models for the transmission of gas data flow, namely single-hop and multi-hop transmission modes, are investigated in depth, and a gas data compressed sensing collection model is built based on a multi-hop network. To utilize the sparse characteristics of gas data, the concept of total variation is introduced and a high-efficiency gas data compression and reconstruction method based on Total Variation Sparsity based on Multi-Hop (TVS-MH) is proposed. According to the simulation results, by using the proposed method, the moving measurement data flow from an underground distributed mobile network can be acquired and transmitted efficiently.
Highlights
During the application of both a coal mine Internet of Things (IoT) mine and many mobile measurement devices, a large amount of moving measurement data is generated, and transferring these amounts of monitoring data efficiently is a challenge
This paper presents a compressed sensing algorithm for coal mine moving measurement data based on a multi-hop network and total variation by taking gas data in the mobile measurement data as an example
Multi-hop networks, a Total Variation Sparsity based on Multi-Hop (TVS-MH) algorithm is proposed, follows
Summary
During the application of both a coal mine Internet of Things (IoT) mine and many mobile measurement devices (e.g., intelligent miner’s lamp), a large amount of moving measurement data is generated, and transferring these amounts of monitoring data efficiently is a challenge. This paper presents a compressed sensing algorithm for coal mine moving measurement data based on a multi-hop network and total variation by taking gas data in the mobile measurement data as an example. In view of the long linear structure of coal mine roadways and the time sparsity of gas sensors, a compression and reconstruction algorithm for a coal mine network transmission structure is studied in this paper. By studying the relationship between data transmission and compressed sensing in Sensors 2018, 18, x FOR PEER REVIEW multi-hop networks, a Total Variation Sparsity based on Multi-Hop (TVS-MH) algorithm is proposed, follows.
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